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Machine Learning - IBM Research Machine Learning Machine learning uses data to teach AI systems to imitate the way that humans learn They can find the signal in the noise of big data, helping businesses improve their operations We've been in the field since since the beginning: IBMer Arthur Samuel even coined the term “Machine Learning” back in 1959
Introducing AI Fairness 360 - IBM Research Machine learning models are increasingly used to inform high-stakes decisions about people Although machine learning, by its very nature, is always a form of statistical discrimination, the discrimination becomes objectionable when it places certain privileged groups at systematic advantage and certain unprivileged groups at systematic disadvantage
Quantum Machine Learning - IBM Research Quantum Machine Learning We now know that quantum computers have the potential to boost the performance of machine learning systems, and may eventually power efforts in fields from drug discovery to fraud detection We're doing foundational research in quantum ML to power tomorrow’s smart quantum algorithms
What is synthetic data? - IBM Research Aim a firehose of data at a human, and you get information overload But if you do the same to a computer, you get machine-learning models that can learn to complete sentences as you type or detect tumors in medical scans that are often too subtle for a human eye to see
When Machine Learning meets Dynamical Systems: Theory and Applications . . . The recent wave of using machine learning to analyze and manipulate real-world systems has inspired many research topics in the joint interface of machine learning and dynamical systems However, the real world applications are diverse and complex with vulnerabilities such as simulation divergence or violation of certain prior knowledge
MILEPOST GCC: Machine learning based research compiler In this paper we describe MILEPOST1 GCC, a machine-learning-based compiler that automatically adjusts its optimization heuristics to improve the execution time, code size, or compilation time of specific programs on different architectures
What is federated learning? - IBM Research In vertical federated learning, the data are complementary; movie and book reviews, for example, are combined to predict someone’s music preferences Finally, in federated transfer learning, a pre-trained foundation model designed to perform one task, like detecting cars, is trained on another dataset to do something else, like identify cats
Neuro-symbolic AI - IBM Research Neuro-symbolic AI We see Neuro-symbolic AI as a pathway to achieve artificial general intelligence By augmenting and combining the strengths of statistical AI, like machine learning, with the capabilities of human-like symbolic knowledge and reasoning, we're aiming to create a revolution in AI, rather than an evolution
Machine Learning for Drug Development and Causal Inference The Machine Learning for Drug Development and Causal Inference group is developing machine learning models for innovative drug discovery technologies and bringing them to fruition for IBM clients Our researchers believe that drug discovery can benefit from technologies that learn from the rich clinical, omics, and molecular data being
Systematic literature review: Quantum machine learning and its . . . Nonetheless, there are other fields like machine learning, finance, or chemistry where quantum computation could be useful with current quantum devices This manuscript aims to present a review of the literature published between 2017 and 2023 to identify, analyze, and classify the different types of algorithms used in quantum machine learning